Exploratory and Predictive Analysis of Ecommerce Dataset
Aveek Choudhury - Harshita Ved - Priyal Maheshwari - Sarang Pande - Sonal Jain
12/2/2019
Overview
E-commerce is a trending business sector, of electronically buying and selling products over the internet. This fastgrowing industry has also adapted features like online payments, internet marketing, door-step delivery, returns and exchanges, etc. Since it is a widespread industry that adds to human convenience and luxuries, there is a lot of scope for improvements and business growth, which brings profits to both consumers and industry.
Understanding the problem
- Analysis on the buying trends of customers and determine if a customer is likely going to buy another product based on what he is currently purchasing
- Based on the reviews given by the customers, we will be rating the products sold by the store by analyzing the text in the reviews
- Visualize the sale throughout brazil on maps based on various seasons
Description of the dataset
This is a Brazilian ecommerce public dataset of orders made at Olist Store. The dataset has information of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. The Dataset contains multiple tables providing information on actual ecommerce purchases along with various other dimensions of a particular purchase like Customer Information, Seller Information, Product Metadata as well as Customer reviews on their purchase experience. The dataset also has geolocation file that relates Brazilian zip codes to latitude/longitude coordinates.
Order status of customers of each state
ordergeotable <- separate(ordergeotable,order_purchase_timestamp , into=c("purchasedate", "purchasetime"), sep=" ")
ordergeotable <- separate(ordergeotable, purchasedate , into=c("year", "month", "date"), sep="-")
geo_data <-na.omit(ordergeotable)
state <- read_state(code_state="all", year=2018) ##
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|=================================================================| 100%g <- ggplot() +
geom_sf(data=state, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE)
#all states
a <- g + geom_point(ordergeotable, mapping = aes(x=geolocation_lng, y=geolocation_lat, color = customer_state), position = "jitter", size=.15, alpha=1/2)+
coord_sf(xlim = c(-70,-30), ylim = c(-40,5), expand = FALSE)+ facet_wrap(~year)+
labs(y="Latitude of location.", x="longitude of location.",title="Mapping of different states of brazil in Map")
plot(a)
Add Title
freq_count <- as.data.frame(table(ordertable$customer_unique_id))
odtbl <- ordertable %>% select_at(vars(year, month, customer_unique_id)) %>%
distinct_at(vars(year,month, customer_unique_id)) %>%
arrange_at(vars(year, month)) %>%
count_(vars(year, month))
odtbl <- odtbl %>% filter(!((month == "09" | month == "10") & year == "2018"))
odd <-odtbl %>% ggplot() + geom_line(mapping = aes(x = month, y = n, group = 1),show.legend = FALSE) +facet_wrap(~year)+geom_point(mapping = aes(x = month, y = n, group = 1))+labs(x="Month", y="Count of new customers added each month", title = "New customers purchased every consecutive month")+theme(axis.text.y= element_text(angle=90))
ggplotly(odd)Overall sales based on holidays
a <- items_data %>% left_join(orders_data) %>% mutate(mny = format(strptime(order_purchase_timestamp, "%Y-%m-%d %H:%M:%S"),'%Y-%m')) %>% group_by(mny) %>% summarise(total = sum(price)) %>% arrange(mny)
brazil_holidays <- brazil_holidays_data %>% mutate(mny = strftime(Date, format = "%Y-%m")) %>% group_by(mny) %>% mutate(holidays_by_week = paste0(Holiday, collapse = ",")) %>% select(mny, holidays_by_week)
brazil_holidays <- brazil_holidays[!duplicated(brazil_holidays$mny),]
p <- plot_ly(a, x = ~mny, y = ~total, type = 'scatter', mode = 'lines')
p <- p %>%
add_trace(
type = 'bar',
x = brazil_holidays$mny,
y = 1000000,
text = brazil_holidays$holidays_by_week,
hoverinfo = 'text',
marker = list(color='yellow'),
showlegend = F,
width = 0.3
) %>% layout(xaxis = list(autotick = F, dtick = 1)) %>%
layout(title = 'Monthly sale along with yearly holidays in Brazil',
xaxis = list(title = 'Time (Year - Month)',
autotick = F, dtick = 1),
yaxis = list(title = 'Total Purchase (in $)'))
pClearly, the sale increases significantly when there is an event during a particular month.
Hour by day transactions
order_weekday <- orders_data %>% mutate(purchase_weekday = wday(order_purchase_timestamp), purchase_hour = format(strptime(order_purchase_timestamp, "%Y-%m-%d %H:%M:%S"),'%H')) %>% group_by(purchase_weekday, purchase_hour) %>% summarise(total_transactions = n())
p <- plot_ly(data = order_weekday,
x = ~purchase_hour,
y = ~purchase_weekday,
z = ~total_transactions,
type = "heatmap",
width = 1050,
height = 500,
colors = colorRamp(c("white","yellow", "red"))) %>%
layout(title = 'Transactions over the hour by day',
xaxis = list(title = 'Hour'),
yaxis = list(title = 'Day', tickvals = c(1, 2, 3, 4, 5, 6, 7), ticktext = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")),
legend = list(title = "Total Transactions")) %>%
add_annotations(x = order_weekday$purchase_hour, y = order_weekday$purchase_weekday, text = order_weekday$total_transactions, xref = 'x', yref = 'y', showarrow = FALSE, font=list(color='black'))
pThe heatmap indiciates that the majority of the transactions occur over the weekdays during office hours. We can thus increases our marketing strategies during this period.
Total payment vs Total Transactions based on payment methods
payment_sum <- payments_data %>% filter(payment_type != "not_defined") %>% group_by(payment_type) %>% summarise(sum = sum(payment_value))
payment_count <- payments_data %>% filter(payment_type != "not_defined") %>% group_by(payment_type) %>% summarise(count = n())
p <- plot_ly() %>%
add_pie(data = payment_count, labels = ~payment_type, values = ~count, domain = list(x = c(0, 0.4), y = c(0.4, 1))) %>%
add_pie(data = payment_sum, labels = ~payment_type, values = ~sum, domain = list(x = c(0.6, 1), y = c(0.4, 1))) %>%
layout(title = "Number of payments vs Total payment values", showlegend = F,
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
pThe market in brazil is heavily dominated by credit card payments. Credit cards are used in ~74% of the total transactions amounting to ~78% of the total market revenue for Olist.
Payment type growth analysis
payment_group <- payments_data %>% filter(payment_type != "not_defined") %>% group_by(order_ID, payment_type) %>% summarise(count = n())
payment_order_group <- payment_group %>% left_join(orders_data) %>% select(order_ID, payment_type, count, order_purchase_timestamp) %>% mutate(purchase_mny = format(strptime(order_purchase_timestamp, "%Y-%m-%d %H:%M:%S"),'%Y-%m'))
payment_abc <- payment_order_group %>% group_by(purchase_mny, payment_type) %>% summarise(total_count = n())%>% ungroup()
p <- plot_ly(payment_abc, x = ~purchase_mny, y = ~total_count, color = ~payment_type, type = 'scatter', mode = 'lines+markers') %>% layout(title = 'Payment type growth monthly',
xaxis = list(title = 'Time (Year - Month)'),
yaxis = list(title = 'Total number of transactions'))
pgeo_data <- distinct(geolocation_data,geolocation_state,geolocation_zip_code_prefix,geolocation_city, .keep_all = TRUE)
# customer's geo location data
customers_geo_data <- customers_data %>%
left_join(geo_data,by=c("customer_state"="geolocation_state",
"customer_city"="geolocation_city",
"customer_zip_code_prefix" ="geolocation_zip_code_prefix")) %>%
rename("customer_geolocation_lat" = "geolocation_lat",
"customer_geolocation_lng" = "geolocation_lng" ) %>%
drop_na(customer_geolocation_lat,customer_geolocation_lng)
# seller's geo location data
sellers_geo_data <- sellers_data %>%
left_join(geo_data,by=c("seller_state"="geolocation_state",
"seller_city"="geolocation_city",
"seller_zip_code_prefix" ="geolocation_zip_code_prefix")) %>%
rename("seller_geolocation_lat" = "geolocation_lat",
"seller_geolocation_lng" = "geolocation_lng" ) %>%
drop_na(seller_geolocation_lat,seller_geolocation_lng)
# data manipulation data frame for sellers and orders delivered by these sellers
seller_orders_data <- items_data %>% left_join(sellers_geo_data, by="seller_ID") %>%
left_join(orders_data, by="order_ID") %>%
left_join(customers_geo_data, by="customer_ID") %>%
drop_na(order_approved_at,order_purchase_timestamp,order_delivered_carrier_date,
order_delivered_customer_date,seller_geolocation_lat,seller_geolocation_lng,
customer_geolocation_lat,customer_geolocation_lng) %>%
filter(order_status == "delivered") %>%
mutate(approved_in_days = round(difftime(strptime(order_approved_at,
format = "%Y-%m-%d %H:%M:%S"),
strptime(order_purchase_timestamp,
format="%Y-%m-%d %H:%M:%S"),
units="days")),
delivered_in_days = round(difftime(strptime(order_delivered_customer_date,
format = "%Y-%m-%d %H:%M:%S"),
strptime(order_purchase_timestamp,
format="%Y-%m-%d %H:%M:%S"),
units="days")),
estimated_in_days = round(difftime(strptime(order_estimated_delivery_date,
format = "%Y-%m-%d"),
strptime(order_purchase_timestamp,
format="%Y-%m-%d %H:%M:%S"),
units="days")),
del_to_carrier_in = round(difftime(strptime(order_delivered_carrier_date,
format = "%Y-%m-%d %H:%M:%S"),
strptime(order_approved_at,
format="%Y-%m-%d %H:%M:%S"),
units="days")),
delay = round(difftime(strptime(order_delivered_customer_date,
format = "%Y-%m-%d %H:%M:%S"),
strptime(order_estimated_delivery_date,
format="%Y-%m-%d"),
units="days"))) %>%
separate(order_purchase_timestamp , into=c("purchase_year",
"purchase_month", "purchase_date"),
sep="-") %>%
separate(purchase_date , into=c("purchase_date", "purchase_time"), sep=" ") %>%
select(seller_ID, seller_city, seller_zip_code_prefix, seller_state,
seller_geolocation_lat, seller_geolocation_lng,
customer_ID, customer_city, customer_state,customer_zip_code_prefix,
customer_geolocation_lat,customer_geolocation_lng,
order_ID, product_ID, approved_in_days,delivered_in_days,
estimated_in_days, del_to_carrier_in, delay,
purchase_year,purchase_month, purchase_date) %>%
mutate(delay = as.factor(if_else(delay <= 0 , 0, 1)))Top 10 Sellers and their Product Categories
# Top 10 sellers
top_sellers <- seller_orders_data %>% group_by(seller_ID) %>%
summarise(total_orders=n()) %>% top_n(10,total_orders)
# Top 10 Product Categories
products_data_english <- left_join(products_data,translations_data, by = c("product_category_name"))
products_data_eng_items <- left_join(items_data,products_data_english, by = "product_ID")
Top10_Product <- products_data_eng_items %>%
filter(!is.na(product_category_name_english)) %>%
group_by(product_category_name_english) %>%
tally(sort = TRUE) %>%
top_n(10)
# Yearly Analysis
yearly_product_sold <- seller_orders_data %>%
left_join(products_data, by="product_ID") %>%
left_join(translations_data,by="product_category_name") %>%
group_by(seller_ID, purchase_year, purchase_month) %>%
filter(seller_ID %in% top_sellers$seller_ID)%>%
mutate(seller_name = paste0("Seller_", seller_ID),
product_category_name_english = ifelse(product_category_name_english %in%
Top10_Product$product_category_name_english,
product_category_name_english, "Others"))
# Plot
top_seller_yearly_categories <- ggplot(yearly_product_sold,
aes(x=purchase_year, fill=product_category_name_english,
name="Product Categories")) +
geom_bar() + facet_grid(. ~ seller_name) +
labs(x = "Year", y= "Product Count")+
guides(fill=guide_legend("Product Categories"))
ggplotly(top_seller_yearly_categories)Customer and Seller Distribution Across Brazil
#Plot all Cutomers and Sellers on Single Plot
seller_loc_data <- distinct(seller_orders_data,seller_ID, .keep_all = TRUE)
seller_loc_data <- seller_loc_data %>% mutate(seller_geolocation_lat = seller_geolocation_lat+0.5) %>%
filter(seller_geolocation_lat > -34, seller_geolocation_lng > -70)
seller_loc_data$img = sample(c("location.png"),nrow(seller_loc_data),replace=TRUE)
customer_loc_data <- distinct(customers_geo_data,customer_geolocation_lat,customer_geolocation_lng, .keep_all = TRUE)
customer_loc_data <- customer_loc_data %>%
mutate(customer_geolocation_lat = customer_geolocation_lat) %>%
filter(customer_geolocation_lat > -34, customer_geolocation_lat < 4,
customer_geolocation_lng > -70)
seller_map <- ggplot() +
geom_sf(data=state, fill="grey", color="brown", size=.15, show.legend = FALSE) +
geom_image(data= seller_loc_data, mapping = aes(x=seller_geolocation_lng,
y=seller_geolocation_lat,
image=img,
label = rownames(seller_loc_data)),
size=0.02) +
facet_wrap(~purchase_year) +
geom_sf_label(data=state, aes(label = abbrev_state),
label.padding= unit(0.15, "lines"), label.size = 0.05) +
labs(x = "Longitude", y= "Latitude")
seller_map
Top 10 Sellers Delivery and Mean-Estimation Time
# Delivery distribution of top 10 sellers
delivery_distribution <- top_sellers %>%
left_join(seller_orders_data,by="seller_ID") %>% group_by(seller_ID) %>%
select(seller_ID,order_ID,approved_in_days,
delivered_in_days,estimated_in_days, del_to_carrier_in)%>%
mutate(seller_name = paste0("Seller_", seller_ID))
# Average Delivery Estimation by top 10 Sellers
mean_delivery_estimate <- delivery_distribution %>% group_by(seller_ID) %>%
summarize(avg_estimated_days = mean(estimated_in_days)) %>%
mutate(seller_name = paste0("Seller_", seller_ID))
# Average Delivery to Carrier
avg_carrier_del_days <- delivery_distribution %>% group_by(seller_ID) %>%
summarize(mean_carrier_del = mean(del_to_carrier_in)) %>%
mutate(seller_name = paste0("Seller_", seller_ID))
# Box plot for Delivery Distribution and Estimated Delivery
delivery_dist_plot <- plot_ly(data = mean_delivery_estimate, x = ~seller_name, y = ~avg_estimated_days,
type='scatter', mode="lines", name ="Estimated Delivery") %>%
add_trace(data= avg_carrier_del_days, x= ~seller_name, y= ~mean_carrier_del,
type='scatter',mode="lines", name ="Carrier Delivery") %>%
add_boxplot(data = delivery_distribution, x = ~seller_name, y = ~delivered_in_days,
color = ~seller_name, type = "box" , name= ~seller_name) %>%
layout(xaxis=list(title = "Top 10 Sellers", type = "category",
categoryorder = "array",
categoryarray = mean_delivery_estimate$seller_name,
size=8),
yaxis=list(title = "Number of Days",
range = c(0,50),
size=8))
delivery_dist_plotloc_data <- seller_orders_data %>% group_by(seller_ID) %>%
summarise(products_sold=n()) %>% top_n(10,products_sold) %>%
left_join(seller_orders_data,by="seller_ID") %>%
group_by(seller_ID, customer_state, customer_city) %>%
mutate(seller_name = paste0("Seller_", seller_ID))
loc_data <- distinct(loc_data,seller_ID,customer_ID, .keep_all = TRUE)
loc_data <- distinct(loc_data,seller_ID,customer_state,customer_city,customer_zip_code_prefix, .keep_all = TRUE)
top_seller_loc_data <- loc_data %>% mutate(seller_geolocation_lat = seller_geolocation_lat+1.5) %>%
group_by(seller_name, seller_geolocation_lat,seller_geolocation_lng) %>%
count()
top_seller_loc_data$img = sample(c("location.png"),size=10,replace=TRUE)
delay_stats <- loc_data %>% group_by(seller_name) %>% summarise(prop = round((sum(delay==1)/n())*100,2))
seller_cust_map <- ggplot() +
geom_sf(data=state, fill="grey", color="brown", size=.15, show.legend = FALSE) +
geom_point(data=loc_data, mapping = aes(x=customer_geolocation_lng,
y=customer_geolocation_lat,
color=delay), position="jitter", size= 1) +
geom_image(data= top_seller_loc_data, mapping = aes(x=seller_geolocation_lng,
y=seller_geolocation_lat,
image=img,
label = rownames(top_seller_loc_data)),
size=0.1) +
facet_wrap(~seller_name) +
geom_text(data = delay_stats, mapping = aes(x = -Inf, y = -Inf,
label = paste0("Delay = ",prop,"%")),
hjust = -0.1, vjust = -1) +
scale_color_manual(breaks = c("1", "0"), labels = c("Delayed","On Time"),
name = "Delivery Status",
values=c("white", "blue")) +
labs(x = "Longitude", y= "Latitude", size=8)
seller_cust_map